Acquisition of motion primitives of robot in human-navigation task towards human-robot interaction based on "quasi-symbols"

    研究成果: Article

    6 引用 (Scopus)

    抄録

    A novel approach to human-robot collaboration based on quasi-symbolic expressions is proposed. The target task is navigation in which a person with his or her eyes covered and a humanoid robot collaborate in a context-dependent manner. The robot uses a recurrent neural net with parametric bias (RNNPB) model to acquire the behavioral primitives, which are sensory-motor units, composing the whole task. The robot expresses the PB dynamics as primitives using symbolic sounds, and the person influences these dynamics through tactile sensors attached to the robot. Experiments with six participants demonstrated that the level of influence the person has on the PB dynamics is strongly related to task performance, the person's subjective impressions, and the prediction error of the RNNPB model (task stability). Simulation experiments demonstrated that the subjective impressions of the correspondence between the utterance sounds (the PB values) and the motions were well reproduced by the rehearsal of the RNNPB model.

    元の言語English
    ページ(範囲)188-196
    ページ数9
    ジャーナルTransactions of the Japanese Society for Artificial Intelligence
    20
    発行部数3
    DOI
    出版物ステータスPublished - 2005

    Fingerprint

    Human robot interaction
    Navigation
    Robots
    Neural networks
    Acoustic waves
    Experiments
    Sensors

    ASJC Scopus subject areas

    • Artificial Intelligence

    これを引用

    @article{9a01ec68e62e4ebfa46dc9ed3e00097f,
    title = "Acquisition of motion primitives of robot in human-navigation task towards human-robot interaction based on {"}quasi-symbols{"}",
    abstract = "A novel approach to human-robot collaboration based on quasi-symbolic expressions is proposed. The target task is navigation in which a person with his or her eyes covered and a humanoid robot collaborate in a context-dependent manner. The robot uses a recurrent neural net with parametric bias (RNNPB) model to acquire the behavioral primitives, which are sensory-motor units, composing the whole task. The robot expresses the PB dynamics as primitives using symbolic sounds, and the person influences these dynamics through tactile sensors attached to the robot. Experiments with six participants demonstrated that the level of influence the person has on the PB dynamics is strongly related to task performance, the person's subjective impressions, and the prediction error of the RNNPB model (task stability). Simulation experiments demonstrated that the subjective impressions of the correspondence between the utterance sounds (the PB values) and the motions were well reproduced by the rehearsal of the RNNPB model.",
    keywords = "Human-Robot Interaction, Motion Primitive, Quasi-Symbol, RNNPB",
    author = "Tetsuya Ogata and Shigeki Sugano and Jun Tani",
    year = "2005",
    doi = "10.1527/tjsai.20.188",
    language = "English",
    volume = "20",
    pages = "188--196",
    journal = "Transactions of the Japanese Society for Artificial Intelligence",
    issn = "1346-0714",
    publisher = "Japanese Society for Artificial Intelligence",
    number = "3",

    }

    TY - JOUR

    T1 - Acquisition of motion primitives of robot in human-navigation task towards human-robot interaction based on "quasi-symbols"

    AU - Ogata, Tetsuya

    AU - Sugano, Shigeki

    AU - Tani, Jun

    PY - 2005

    Y1 - 2005

    N2 - A novel approach to human-robot collaboration based on quasi-symbolic expressions is proposed. The target task is navigation in which a person with his or her eyes covered and a humanoid robot collaborate in a context-dependent manner. The robot uses a recurrent neural net with parametric bias (RNNPB) model to acquire the behavioral primitives, which are sensory-motor units, composing the whole task. The robot expresses the PB dynamics as primitives using symbolic sounds, and the person influences these dynamics through tactile sensors attached to the robot. Experiments with six participants demonstrated that the level of influence the person has on the PB dynamics is strongly related to task performance, the person's subjective impressions, and the prediction error of the RNNPB model (task stability). Simulation experiments demonstrated that the subjective impressions of the correspondence between the utterance sounds (the PB values) and the motions were well reproduced by the rehearsal of the RNNPB model.

    AB - A novel approach to human-robot collaboration based on quasi-symbolic expressions is proposed. The target task is navigation in which a person with his or her eyes covered and a humanoid robot collaborate in a context-dependent manner. The robot uses a recurrent neural net with parametric bias (RNNPB) model to acquire the behavioral primitives, which are sensory-motor units, composing the whole task. The robot expresses the PB dynamics as primitives using symbolic sounds, and the person influences these dynamics through tactile sensors attached to the robot. Experiments with six participants demonstrated that the level of influence the person has on the PB dynamics is strongly related to task performance, the person's subjective impressions, and the prediction error of the RNNPB model (task stability). Simulation experiments demonstrated that the subjective impressions of the correspondence between the utterance sounds (the PB values) and the motions were well reproduced by the rehearsal of the RNNPB model.

    KW - Human-Robot Interaction

    KW - Motion Primitive

    KW - Quasi-Symbol

    KW - RNNPB

    UR - http://www.scopus.com/inward/record.url?scp=18544372891&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=18544372891&partnerID=8YFLogxK

    U2 - 10.1527/tjsai.20.188

    DO - 10.1527/tjsai.20.188

    M3 - Article

    AN - SCOPUS:18544372891

    VL - 20

    SP - 188

    EP - 196

    JO - Transactions of the Japanese Society for Artificial Intelligence

    JF - Transactions of the Japanese Society for Artificial Intelligence

    SN - 1346-0714

    IS - 3

    ER -